Libraries
library(tidyverse)
library(gapminder)
library(plotly)
library(htmlwidgets)
Downloading data
billboard <- read.csv("https://raw.githubusercontent.com/reisanar/datasets/master/all_billboard_summer_hits.csv")
head(billboard)
Interactive plot: Danceability vs. Energy
gapminder_billboard <- filter(billboard,
mode == "major")
billboard_plot <- ggplot(
data = gapminder_billboard,
mapping = aes(x = danceability, y = energy,
color = time_signature)) +
geom_point() +
scale_x_log10() +
theme_minimal()
ggplotly(billboard_plot)
Model: Loudness vs. Energy
model_billboard <- lm(loudness ~ energy, data = billboard)
summary(model_billboard)
Call:
lm(formula = loudness ~ energy, data = billboard)
Residuals:
Min 1Q Median 3Q Max
-10.1307 -1.6136 0.2662 1.9161 6.0742
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -16.4940 0.3509 -47.01 <2e-16 ***
energy 12.7112 0.5384 23.61 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.562 on 598 degrees of freedom
Multiple R-squared: 0.4824, Adjusted R-squared: 0.4815
F-statistic: 557.3 on 1 and 598 DF, p-value: < 2.2e-16
ggplot(billboard, aes(x = energy, y = loudness)) +
geom_point() +
geom_smooth(method = "lm",
formula = "y ~ x") +
annotate(geom = "text",
x = 0.75, y = -19,
label = "An increase in energy of music is \n associated with an increase in loudness")

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